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choosing the right azure data platform: synapse, fabric, databricks or ADF for cloud based ETL and PowerBI integration.

Gabriel25 525 Reputation points
2025-04-17T12:22:13.4366667+00:00

Our organization is currently migrating to the cloud and building a new data infrastructure in Microsoft Azure. The plan involves migrating large volumes of data (millions of records across multiple clients), developing ETL pipelines, and integrating the processed data with Power BI dashboards to derive actionable insights.

As we are fully adopting the Microsoft ecosystem, we are evaluating the most suitable technologies and architecture for this setup.

Could you please advise on the optimal approach for the following?

1.	Storage and Analytics Layer: Should we go with Data Lake, Data Warehouse, or Datamarts for our data storage and analytics needs?

2.	Processing and Orchestration: Among Azure Synapse Analytics, Microsoft Fabric, Azure Databricks, and Azure Data Factory, which is best suited for:

◦	Handling large-scale ETL workloads,

◦	Supporting multi-client data processing,

◦	Seamlessly integrating with Power BI?

We’re looking for guidance based on scalability, performance, cost-efficiency, and native integration with Power BI.

Any insights or recommendations would be greatly appreciated!

Azure Data Factory
Azure Data Factory
An Azure service for ingesting, preparing, and transforming data at scale.
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  1. Smaran Thoomu 24,110 Reputation points Microsoft External Staff Moderator
    2025-04-17T12:52:09.9566667+00:00

    Hi @Gabriel-2005
    Since your organization is moving to Azure and planning to build an end-to-end modern data platform, here's a breakdown of what you might consider for each component:

    Storage & Analytics Layer

    • Data Lake (Azure Data Lake Storage Gen2): Ideal for storing raw or semi-structured data at scale. Best suited for a lakehouse architecture when combined with tools like Fabric or Databricks.
    • Data Warehouse (e.g., Synapse Dedicated SQL Pools or Fabric Warehouse): Best when you need structured, performant, and governed reporting—especially for Power BI semantic models.
    • Datamarts (within Power BI or Fabric): Useful for business users and smaller self-service reporting needs. Not ideal for large-scale enterprise processing.

    Recommendation: Use a lakehouse approach—store raw data in a Data Lake, process and model it in Fabric or Databricks, and serve it to Power BI via Fabric Warehouse or Direct Lake.

    ETL & Data Processing Tools

    Here's how the tools compare in your context:

    Azure Data Factory (ADF):

    • Best for ETL orchestration, especially when moving data between sources.
    • Low-code interface.
    • Doesn’t handle in-memory transformations well; use Mapping Data Flows or push data into Synapse/Fabric for that.

    Azure Synapse Analytics:

    • All-in-one solution with SQL-based querying, pipelines, and Spark.
    • Good if you need SQL and Spark in one place, though some parts are being replaced by Microsoft Fabric.

    Azure Databricks:

    • Excellent for large-scale, complex transformations, ML workloads, and code-driven ETL (PySpark/Scala).
    • Great for multi-client data processing and lakehouse architecture.

    Microsoft Fabric:

    • Microsoft’s latest unified data platform.
    • Supports Data Factory-style pipelines, Data Lake, Notebooks, Data Warehouses, and tight Power BI integration.
    • Best suited if you're heavily invested in the Microsoft ecosystem.

    Recommendation:

    • If you prefer low-code and easy integration with Power BI, Microsoft Fabric is a future-ready choice.
    • If you have advanced data engineering and ML needs, Databricks + Data Lake + Power BI is still very strong.
    • Use ADF or Fabric Pipelines for orchestration.

    Hope this helps. Do let us know if you any further queries.

    If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.

    1 person found this answer helpful.

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